{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Causal Tracing (ROME)\n",
    "\n",
    "Causal tracing was a methodology for locating where facts are stored in transformer LMs, introduced in the paper [\"Locating and Editing Factual Associations in GPT\" (Meng et al., 2023)](https://arxiv.org/abs/2202.05262). In this notebook, we will implement their method using this library and replicate the first causal tracing example in the paper (full figure 1 on page 2)."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/frankaging/pyvene/blob/main/tutorials/advance_tutorials/Causal_Tracing.ipynb)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "__author__ = \"Aryaman Arora\"\n",
    "__version__ = \"11/08/2023\""
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Set-up"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "try:\n",
    "    # This library is our indicator that the required installs\n",
    "    # need to be done.\n",
    "    import pyvene\n",
    "\n",
    "except ModuleNotFoundError:\n",
    "    !pip install git+https://github.com/stanfordnlp/pyvene.git"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import torch\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "from pyvene import embed_to_distrib, top_vals, format_token\n",
    "from pyvene import (\n",
    "    IntervenableModel,\n",
    "    VanillaIntervention, Intervention,\n",
    "    RepresentationConfig,\n",
    "    IntervenableConfig,\n",
    "    ConstantSourceIntervention,\n",
    "    LocalistRepresentationIntervention\n",
    ")\n",
    "from pyvene import create_gpt2\n",
    "\n",
    "%config InlineBackend.figure_formats = ['svg']\n",
    "from plotnine import (\n",
    "    ggplot,\n",
    "    geom_tile,\n",
    "    aes,\n",
    "    facet_wrap,\n",
    "    theme,\n",
    "    element_text,\n",
    "    geom_bar,\n",
    "    geom_hline,\n",
    "    scale_y_log10,\n",
    "    xlab, ylab, ylim,\n",
    "    scale_y_discrete, scale_y_continuous, ggsave\n",
    ")\n",
    "from plotnine.scales import scale_y_reverse, scale_fill_cmap\n",
    "from tqdm import tqdm\n",
    "\n",
    "titles={\n",
    "    \"block_output\": \"single restored layer in GPT2-XL\",\n",
    "    \"mlp_activation\": \"center of interval of 10 patched mlp layer\",\n",
    "    \"attention_output\": \"center of interval of 10 patched attn layer\"\n",
    "}\n",
    "\n",
    "colors={\n",
    "    \"block_output\": \"Purples\",\n",
    "    \"mlp_activation\": \"Greens\",\n",
    "    \"attention_output\": \"Reds\"\n",
    "} "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Factual recall\n",
    "\n",
    "Let's set up the model (gpt2-xl) and test it on the fact we want to causal trace: \"The Space Needle is in downtown **Seattle**\"."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "loaded model\n",
      "The Space Needle is in downtown\n",
      "_Seattle             0.9763794541358948\n",
      "_Bellev              0.0027682818472385406\n",
      "_Portland            0.0021577849984169006\n",
      ",                    0.0015149445971474051\n",
      "_Vancouver           0.0014351375866681337\n",
      "_San                 0.0013575783232226968\n",
      "_Minneapolis         0.000938268203753978\n",
      ".                    0.0007443446083925664\n",
      "_Tacoma              0.0006097281584516168\n",
      "_Washington          0.0005885555874556303\n"
     ]
    }
   ],
   "source": [
    "device = \"cuda:0\" if torch.cuda.is_available() else \"cpu\"\n",
    "config, tokenizer, gpt = create_gpt2(name=\"gpt2-xl\")\n",
    "gpt.to(device)\n",
    "\n",
    "base = \"The Space Needle is in downtown\"\n",
    "inputs = [\n",
    "    tokenizer(base, return_tensors=\"pt\").to(device),\n",
    "]\n",
    "print(base)\n",
    "res = gpt(**inputs[0])\n",
    "distrib = embed_to_distrib(gpt, res.last_hidden_state, logits=False)\n",
    "top_vals(tokenizer, distrib[0][-1], n=10)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Corrupted run\n",
    "\n",
    "The first step in implementing causal tracing is to corrupt the input embeddings for the subject tokens by adding Gaussian noise to them. In Meng et al., the standard deviation of the Gaussian we sample from is computed as thrice the standard deviation of embeddings over a big dataset. We encode this as a constant, `self.noise_level`.\n",
    "\n",
    "Note that the `source` argument is ignored unlike in causal interventions, since we are adding noise without reference to any other input.\n",
    "\n",
    "Our intervention config intervenes on the `block_input` of the 0th layer, i.e. the embeddings."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "class NoiseIntervention(ConstantSourceIntervention, LocalistRepresentationIntervention):\n",
    "    def __init__(self, embed_dim, **kwargs):\n",
    "        super().__init__()\n",
    "        self.interchange_dim = embed_dim\n",
    "        rs = np.random.RandomState(1)\n",
    "        prng = lambda *shape: rs.randn(*shape)\n",
    "        self.noise = torch.from_numpy(\n",
    "            prng(1, 4, embed_dim)).to(device)\n",
    "        self.noise_level = 0.13462981581687927\n",
    "\n",
    "    def forward(self, base, source=None, subspaces=None):\n",
    "        base[..., : self.interchange_dim] += self.noise * self.noise_level\n",
    "        return base\n",
    "\n",
    "    def __str__(self):\n",
    "        return f\"NoiseIntervention(embed_dim={self.embed_dim})\"\n",
    "\n",
    "\n",
    "def corrupted_config(model_type):\n",
    "    config = IntervenableConfig(\n",
    "        model_type=model_type,\n",
    "        representations=[\n",
    "            RepresentationConfig(\n",
    "                0,              # layer\n",
    "                \"block_input\",  # intervention type\n",
    "            ),\n",
    "        ],\n",
    "        intervention_types=NoiseIntervention,\n",
    "    )\n",
    "    return config"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's check that this reduced the probability of the output \"_Seattle\"."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "_Los                 0.03294256329536438\n",
      "_San                 0.03194474056363106\n",
      "_Seattle             0.026176469400525093\n",
      "_Toronto             0.02585919387638569\n",
      "_Chicago             0.024749040603637695\n",
      "_Houston             0.024224288761615753\n",
      "_Atlanta             0.01866454817354679\n",
      "_Austin              0.017735302448272705\n",
      "_St                  0.017606761306524277\n",
      "_Denver              0.01740877516567707\n"
     ]
    }
   ],
   "source": [
    "base = tokenizer(\"The Space Needle is in downtown\", return_tensors=\"pt\").to(device)\n",
    "config = corrupted_config(type(gpt))\n",
    "intervenable = IntervenableModel(config, gpt)\n",
    "_, counterfactual_outputs = intervenable(\n",
    "    base, unit_locations={\"base\": ([[[0, 1, 2, 3]]])}\n",
    ")\n",
    "distrib = embed_to_distrib(gpt, counterfactual_outputs.last_hidden_state, logits=False)\n",
    "top_vals(tokenizer, distrib[0][-1], n=10)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Restored run\n",
    "\n",
    "We now make a config that performs the following:\n",
    "1. Corrupt input embeddings for some positions.\n",
    "2. Restore the hidden state at a particular layer for some (potentially different positions).\n",
    "\n",
    "This is how Meng et al. check where in the model the fact moves through."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [],
   "source": [
    "def restore_corrupted_with_interval_config(\n",
    "    layer, stream=\"mlp_activation\", window=10, num_layers=48):\n",
    "    start = max(0, layer - window // 2)\n",
    "    end = min(num_layers, layer - (-window // 2))\n",
    "    config = IntervenableConfig(\n",
    "        representations=[\n",
    "            RepresentationConfig(\n",
    "                0,       # layer\n",
    "                \"block_input\",  # intervention type\n",
    "            ),\n",
    "        ] + [\n",
    "            RepresentationConfig(\n",
    "                i,       # layer\n",
    "                stream,  # intervention type\n",
    "        ) for i in range(start, end)],\n",
    "        intervention_types=\\\n",
    "            [NoiseIntervention]+[VanillaIntervention]*(end-start),\n",
    "    )\n",
    "    return config"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now let's run this over all layers and positions! We will corrupt positions 0, 1, 2, 3 (\"The Space Needle\", i.e. the subject of the fact) and restore at a single position at every layer."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "7312\n"
     ]
    }
   ],
   "source": [
    "# should finish within 1 min with a standard 12G GPU\n",
    "token = tokenizer.encode(\" Seattle\")[0]\n",
    "print(token)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "for stream in [\"block_output\", \"mlp_activation\", \"attention_output\"]:\n",
    "    data = []\n",
    "    for layer_i in tqdm(range(gpt.config.n_layer)):\n",
    "        for pos_i in range(7):\n",
    "            config = restore_corrupted_with_interval_config(\n",
    "                layer_i, stream, \n",
    "                window=1 if stream == \"block_output\" else 10\n",
    "            )\n",
    "            n_restores = len(config.representations) - 1\n",
    "            intervenable = IntervenableModel(config, gpt)\n",
    "            _, counterfactual_outputs = intervenable(\n",
    "                base,\n",
    "                [None] + [base]*n_restores,\n",
    "                {\n",
    "                    \"sources->base\": (\n",
    "                        [None] + [[[pos_i]]]*n_restores,\n",
    "                        [[[0, 1, 2, 3]]] + [[[pos_i]]]*n_restores,\n",
    "                    )\n",
    "                },\n",
    "            )\n",
    "            distrib = embed_to_distrib(\n",
    "                gpt, counterfactual_outputs.last_hidden_state, logits=False\n",
    "            )\n",
    "            prob = distrib[0][-1][token].detach().cpu().item()\n",
    "            data.append({\"layer\": layer_i, \"pos\": pos_i, \"prob\": prob})\n",
    "    df = pd.DataFrame(data)\n",
    "    df.to_csv(f\"./tutorial_data/pyvene_rome_{stream}.csv\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The plot below should now replicate Meng et al."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/u/nlp/anaconda/main/anaconda3/envs/wuzhengx-bootleg/lib/python3.8/site-packages/plotnine/ggplot.py:587: PlotnineWarning: Saving 5 x 4 in image.\n",
      "/u/nlp/anaconda/main/anaconda3/envs/wuzhengx-bootleg/lib/python3.8/site-packages/plotnine/ggplot.py:588: PlotnineWarning: Filename: ./tutorial_data/pyvene_rome_block_output.pdf\n"
     ]
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 500x400 with 1 Axes>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 400,
       "width": 500
      }
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/u/nlp/anaconda/main/anaconda3/envs/wuzhengx-bootleg/lib/python3.8/site-packages/plotnine/ggplot.py:587: PlotnineWarning: Saving 5 x 4 in image.\n",
      "/u/nlp/anaconda/main/anaconda3/envs/wuzhengx-bootleg/lib/python3.8/site-packages/plotnine/ggplot.py:588: PlotnineWarning: Filename: ./tutorial_data/pyvene_rome_mlp_activation.pdf\n"
     ]
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 500x400 with 1 Axes>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 400,
       "width": 500
      }
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/u/nlp/anaconda/main/anaconda3/envs/wuzhengx-bootleg/lib/python3.8/site-packages/plotnine/ggplot.py:587: PlotnineWarning: Saving 5 x 4 in image.\n",
      "/u/nlp/anaconda/main/anaconda3/envs/wuzhengx-bootleg/lib/python3.8/site-packages/plotnine/ggplot.py:588: PlotnineWarning: Filename: ./tutorial_data/pyvene_rome_attention_output.pdf\n"
     ]
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 500x400 with 1 Axes>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 400,
       "width": 500
      }
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    }
   ],
   "source": [
    "for stream in [\"block_output\", \"mlp_activation\", \"attention_output\"]:\n",
    "    df = pd.read_csv(f\"./tutorial_data/pyvene_rome_{stream}.csv\")\n",
    "    df[\"layer\"] = df[\"layer\"].astype(int)\n",
    "    df[\"pos\"] = df[\"pos\"].astype(int)\n",
    "    df[\"p(Seattle)\"] = df[\"prob\"].astype(float)\n",
    "\n",
    "    custom_labels = [\"The*\", \"Space*\", \"Need*\", \"le*\", \"is\", \"in\", \"downtown\"]\n",
    "    breaks = [0, 1, 2, 3, 4, 5, 6]\n",
    "\n",
    "    plot = (\n",
    "        ggplot(df, aes(x=\"layer\", y=\"pos\"))    \n",
    "\n",
    "        + geom_tile(aes(fill=\"p(Seattle)\"))\n",
    "        + scale_fill_cmap(colors[stream]) + xlab(titles[stream])\n",
    "        + scale_y_reverse(\n",
    "            limits = (-0.5, 6.5), \n",
    "            breaks=breaks, labels=custom_labels) \n",
    "        + theme(figure_size=(5, 4)) + ylab(\"\") \n",
    "        + theme(axis_text_y  = element_text(angle = 90, hjust = 1))\n",
    "    )\n",
    "    ggsave(\n",
    "        plot, filename=f\"./tutorial_data/pyvene_rome_{stream}.pdf\", dpi=200\n",
    "    )\n",
    "    print(plot)"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.12"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}
